In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …

The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …

Realistic stochastic simulation of hydro‐environmental fluxes in space and time, such as
rainfall, is challenging yet of paramount importance to inform environmental risk analysis …

Modeling the spatial correlation structure of coregionalized data is a frequent task in
numerous fields of the natural sciences. Even in the isotropic case, experimental …

This paper focuses on vector random fields defined on S^d*R^k, d≧2 and k≧1, with
covariance functions that depend on the geodesic distance in S^d and on the separation …

Two algorithms are proposed to simulate space-time Gaussian random fields with a
covariance function belonging to an extended Gneiting class, the definition of which …

Multivariate random fields allow to simultaneously model multiple spatially indexed
variables, playing a fundamental role in geophysical, environmental, and climate disciplines …